Dynamic sensing: Better classification under acquisition constraints

In many machine learning applications the quality of the data is limited by resource constraints (may it be power, bandwidth, memory, ⋯). In such cases, the constraints are on the average resources allocated, therefore there is some control over each sample's quality. In most cases this option remains unused and the data's quality is uniform over the samples. In this paper we propose to actively allocate resources to each sample such that resources are used optimally overall. We propose a method to compute the optimal resource allocation. We further derive generalization bounds for the case where the problem's model is unknown. We demonstrate the potential benefit of this approach on both simulated and real-life problems.